Choosing Your Approach - Workflows vs Agents
Large Language Models (LLMs) excel at more than text generation. They can power systems that automate complex, multi-step tasks. However, building effective automated systems requires choosing the right architecture. The two primary approaches are structured Workflows and dynamic Agents.
This tutorial dissects these core architectural patterns. Its purpose is to equip AI/ML engineers with the understanding necessary to select the most effective pattern - predictable workflow or adaptive agent - for a given automation challenge.
Effective workflows and agents are built upon the foundational capabilities explored in the Sharpen Your AI Toolkit tutorial:
- Memory: Maintaining context across interactions.
- Tool Use: Enabling interaction with external systems.
- Structured Output: Ensuring predictable data formats.
Here, we focus on the conceptual differences and decision criteria for choosing between workflows and agents. The practical implementation details, using libraries like LangChain and LangGraph, are covered in subsequent tutorials: Teamwork Makes the Dream Work - Build Agentic Workflow and Thinking and Acting - Build an AI Agent.
Tutorial Goals
- Differentiate between structured agentic workflows and autonomous agents
- Identify the core components and capabilities enabling these systems
- Learn the key architectural patterns for building workflows
- Understand the decision-making loop and capabilities of autonomous agents
- Establish clear criteria for selecting the appropriate pattern for specific use cases
- Recognize the importance of human-in-the-loop oversight